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Computational Geometry, Fall 2005–2006

Computational Geometry

Lecture: Wednesday, 16:00–19:00, Schreiber 006

Instructor: Dan Halperin, [email protected]
Office hours by appointment

Teaching Assistant: Eitan Yaffe

The course covers fundamental algorithms for solving geometric problems such as computing convex hulls, intersection of line segments, Voronoi diagrams, polygon triangulation, and linear programming in low dimensional space. We will also discuss several applications of geometric algorithms to solving problems in robotics, GIS (geographic information systems), computer graphics, and more.


The main textbook of the course is:

A bibliographic list for the course

Assignments, Examination, and Grades

Course Summary

Below you’ll find a very brief summary of what was covered in class during the semester. This should not be taken as a complete description of the course’s contents.

For an outline of the course, see for example, the course summary in the 2004 computational geometry course.

  • 07.11.05 Introduction
    What is Computational Geometry? Course outline, motivation and techniques. Naive convex hull algorithms (see Chapter 3 in O’Rourke’s book). Orientation (Side-of-line) test.
    Video segments:

    • Searching with an Autonomous Robot
      Sandor P. Fekete, Rolf Klein, and Andreas Nuchter, SoCG 2004, video [link]
    • Exact Minkowski Sums of Convex Polyhedra
      Efi Fogel and Dan Halperin, SoCG 2005, video [lmp4]
  • 9.11.05 Convex hull computation continued; Line segment intersection
    • Gift wrapping, Graham’s \(O(n \log n)\) algorithm (Chapter 1 in CGAA). Lower bound for convex hull algorithms<\li>
    • Output sensitive algorithm to compute the intersections of line segments: Bentley Ottmann’s plane sweep (Chapter 2 in CGAA)
  • 16.11.05 Sweep line; DCEL; Map overlay; Douglas-Peuker polyline simplefication
    • The doubly-connected edge list (DCEL), and overlay of planar subdivisions (CGAA, Chapter 2)
    • The Douglas-Peuker algorithm for line simplification. Video segment no. 1 in CG Video Review 1994, An \(O(n\log n)\) implementation of the Douglas-Peucker algorithm for line simplification, by Hershberger and Snoeyink. For details see Proc. of the 10th ACM Symposium On Computational geometry, pp. 383–384
    • The last segment in CG Video Review 1992, by Tal, Chazelle, and Dobkin. Based on the paper: An optimal algorithm for intersecting line segments in the plane, by Chazelle and Edelsbrunner, J. of the ACM, Vol. 39, 1992, pp. 1–54. For recent advances on this problem see “Notes and comments” at the end of Chapter 2 of CGAA
  • 23.11.05 Polygon triangulation; Art gallery problems
    • Introduction to triangulation of polygons.
    • We have shown that \(\lfloor n/3 \rfloor\) guards are sometimes necessary and always sufficient to guard a simple polygon with \(n\) vertices. We have shown that every simple polygon can be triangulated; that the triangulation graph is 3-colorable; that the dual of the triangulation graph is a tree.
    • A naive algorithm that mimics the triangulation existence proof can triangulate a simple polygon with n vertices in \(O(n^2)\) time. A more efficient algorithm does it in O(n\log n) by first decomposing a polygon into monotone polygons and then triangulating each of the resulting monotone subpolygons
    • The material covered in class could be found in O’Rourke’s book, Chapters 1 and 2, and in CGAA, Chapter 3. (The description of how to decompose a polygon into y-monotone polygons follows O’Rourke’s presentation)
    • Guest talk: Jur van den Berg from Utrecht University on a variety of porblems in robot motion planning and automation
  • 30.11.05 Tetrahedralization; Intersection of halfplanes; Casting
    • We completed the triagulation algorithm. Next, a few facts about tetrahedralization where surveyed: not every polyhedron is tetrahedralizable with vertices of the polyhedron only (see also Assignment no. 2); in general putting guards at the vertices of a polyhedron is insufficient for guarding the polyhedron; there are polyhedra with \(n\) vertices that require \(\Omega(n^{3/2}))\) guards
    • We have shown an \(O(n\log n)\) algorithm for computing the intersection of \(n\) halfplanes. This question was motivated by a problem in casting: computing whether a given polyhedron is castable. See Chapter 4 in CGAA
  • 07.12.05 Linear Programming
    • We presented the linear programming problem, then a randomized incremental algorithm for LP in 2D, running in expected linear time, and finally “backward analysis” for randomized algorithms. See Chapter 4 of CGAA.Randomized linear time algorithm for finding the minimum enclosing disc of a set of points in the plane. Sketch of LP in 3D. CGAA, Chapter 4
  • 14.12.05 Linear Programming cont’d; Orthogonal range search
    • Linear time LP by Meggido, from Preparata-Shamos, Section 7.2.5
    • Orthogonal range search I: kd-tress. CGAA, Chapter 5
  • 21.12.05 Orthogonal range-search cont’d; introduction to Point Location
    • Range tree, fractional cascading. CGAA, Chapter 5
    • Trapezoidal decomposition of planar maps revisited. Simple (but inefficient) solutions to the point location problem
  • 28.12.05 Point location; introduction to Voronoi diagrams
    • History over trapezoidal map structure for point location in expected logarithmic time. Relaxing the general position assumption. CGAA, Chapter 6
    • What are Voronoi diagrams. Basic properties of the standard (\(L_2\)) diagram of point sites in the plane. CGAA, Chapter 7
  • 4.01.06 Voronoi diagrams
    • Further properties of the standard diagram of point sites in the plane. Fortune’s sweep algorithm for computing the diagram in time \(O(n\log n)\). CGAA, Chapter 7
    • Video segments: (i) “Visualizing Fortune’s sweepline algorithm” by Seth Teller and (ii) “Moving a disc between polygons” by Stefan Schirra (both from the ACM CG Video Review 1993)
  • 11.01.06 Arrangements of lines; duality Point-line duality
      • Incremental construction of arrangements of lines. CGAA, Chapter 8
    • Computing the smallest area triangle determined by a set of points. Edelsbrunner’s book, Section 12.4
  • 18.01.06 Discrepancy; introduction to triangulations; overveiw of CGAL
    • Halfplane discrepancy. CGAA, Chapter 8
    • Introduction to triangulation of point sets
    • Guest talk by Efi Fogel; The hitchhiker’s guide to CGAL
  • 25.01.06 Delaunay triangulation
    • Legal triangulations, Delaunay triangulations, RIC of Delaunay. CGAA Chapter 9
The main textbook used in the course:

M. de Berg, M. van Kreveld, M. Overmars, and O. Schwarzkopf,
Computational Geometry: Algorithms and Applications , 3rd Edition
Springer, 2008.

J. O’Rourke,
Computational Geometry in C 2nd Edition
Cambridge University Press, 1998 


J.-D. Boissonnat and M. Yvinec
Algorithmic Geometry
Cambridge University Press, 1995, 1998 (English version)
J.E. Goodman , J. O’Rourke, and Csaba Toth (editors)
Handbook of Discrete and Computational Geometry , 3rd Edition
CRC Press LLC, Boca Raton, FL, 2017.


J.-R. Sack and J. Urrutia (editors)
Handbook of Computational Geometry
North Holland, 2000.


M. Sharir and P.K. Agarwal
Davenport-Schinzel Sequences and Their Geometric Applications
Cambridge University Press, New York, 1995


K. Mulmuley
Computational Geometry: An Introduction Through Randomized Algorithms
Prentice Hall, Englewood Cliffs, NJ, 1994


F.P. Preparata and M.I. Shamos
Computational Geometry: An Introduction
Springer-Verlag, New York, NY, 1985.


H. Edelsbrunner
Algorithms in Combinatorial Geometry Springer Verlag, Heidelberg, 1987.

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